Method of measuring pattern dimension and method of controlling semiconductor device process

ABSTRACT

This invention provides a method of measuring semiconductor pattern dimensions capable of realizing a stable and highly precise pattern dimension measurement technique even when the pattern cross-sectional shapes are changed and making the calculation amount relatively small to reduce the calculation time. More specifically, the relationship between cross-sectional shapes of a pattern and measurement errors in a specified image processing technique is evaluated in advance by the electron beam simulation in a pattern measurement system in a length measuring SEM, and in the actual dimension measurement, dimensions of an evaluation objective pattern are measured from image signals of a scanning electron microscope, and errors of the dimensional measurement of the evaluation objective pattern are estimated and revised based on the relationship between cross-sectional shapes of a pattern and measurement errors evaluated in advance, thereby realizing highly precise measurement where dimensional errors depending on pattern solid shapes are eliminated.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority from Japanese Patent ApplicationJP 2003-397364 filed on Nov. 27, 2003, the content of which is herebyincorporated by reference into this application.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to a manufacturing technique of asemiconductor device. More specifically, it relates to a techniqueeffectively applied to a method and a system for evaluating processedshape conditions of a circuit pattern formed on a wafer by the use ofelectron beam images of the circuit pattern in a semiconductormanufacturing process.

BACKGROUND OF THE INVENTION

Examinations by the inventors of the present invention have learned thatmeasurement and control of pattern dimensions by the use of anelectronic microscope exclusive for the measurement (length measuringSEM) is generally conducted today in the semiconductor manufacturingprocesses. The measurement of pattern dimensions has been automated byapplying image processing technologies to acquired images of the lengthmeasuring SEM, and therefore, expert skills of operators have becomeunnecessary, and measurement variance due to the individual differenceshas been decreased. Objectives of such pattern measurement are mostlypatterns of a resist, an insulating film, polysilicon and the like, andthe width of wire, diameters of circular holes and so forth aremeasured.

An example of the measurement techniques is shown in FIGS. 14A, 14B and14C. Image signals of the SEM is changed according to the pattern shapesand materials, and they shine brightly especially at edge portions of apattern. FIG. 14 shows an example of processing a signal waveform of awire shape pattern. In the signal waveform, two peaks with large signalamounts correspond to edge portions of the wire, and the edge positionsare defined in the manner as shown in FIG. 14 so as to measure thedimensions of the objective pattern. The technique of FIG. 14A is amethod to detect the maximum inclined position of a peak (maximumgradient method), FIG. 14B shows a threshold method to detect an edgeposition by the use of a specified threshold value (th), and FIG. 14Cshows a linear approximation method in which a straight line is appliedto an edge portion and a base portion and a point of intersectiontherebetween is detected.

In the prior dimension measurement method using the SEM images and imageprocessing technologies as described above, peaks and positions of imagesignal waveforms and signal amounts or changes thereof are used todetermine the positions to be measured. However, in these techniques, itis not possible to precisely grasp which portion an actually measureddimension corresponds to in an actual cross section (a top portion, abottom portion or other of the pattern). Especially, in the case where across-sectional shape of the pattern changes, errors in dimensions to bemeasured become different depending on the cross-sectional shape of thepattern, which has been a problem with the prior art.

FIG. 15 shows an example of influences that the changes incross-sectional shapes of the pattern give to the measurement, practicedin 2002 by Villarrubia et al. (Scanning electron microscope analog ofscatterometry”, Proc. SPIE 4689, pp. 304–312). FIG. 15 shows an exampleof simulation illustrating the case where dimensions are measured by thethreshold method (threshold value 50%), in which errors at the measuredposition and the actual bottom position in the cross section aredifferent between the case where a pattern sidewall is vertical (leftside of FIG. 15) and the case where it is inclined (right side of FIG.15). Such positional difference comes from the fact that the measurementalgorithm in the prior length measuring SEM does not consider how asignal waveform changes according to the differences of patterncross-sectional shapes.

FIG. 16 shows relationships between the tilt angle of the patternsidewall (horizontal axis of FIG. 16) and pattern dimension measurementerrors (vertical axis of FIG. 16) by various image processing algorithms(max. Deriv., Regressiont, Sigmoid, Model-Based Lib.), and illustratesthat the measurement errors change depending on cross-sectional shapesof the pattern and the algorithms. Along with the scaling down in thesemiconductor manufacturing processes, influences that the measurementerrors according to the pattern shapes give to the process control havebecome more and more significant. Therefore, it is necessary to solvesuch errors and realize dimension measurement with small errors.Further, for the achievement of higher precision in the process, it isrequired not only to realize the dimension measurement with small errorsbut also to realize the quantitative evaluations of errors incross-sectional shapes as shown in FIG. 15.

In other words, as a technology for solving the technical problemconcerning FIG. 15, Villarrubia et al. have proposed a measurementmethod using an electron beam simulation. This is a method in whichsignal waveforms in which errors in the cross section of the pattern aretaken into consideration are generated by an electron beam simulationand thereby creating libraries, and the signal waveforms of actual SEMare compared with the waveforms in the libraries, and an actualcross-sectional shape of the pattern is estimated from similarwaveforms, and then, the correct dimensions are calculated. TheModel-Based Lib. in FIG. 16 is the evaluation result of the measurementerrors, and a more precise measurement than other techniques can beachieved. In this way, by this technique, it is possible to reduce themeasurement errors due to the cross-sectional shapes and to evaluate thecross-sectional shapes. However, it is required to prepare in advancethe signal waveforms of SEM to various cross-sectional shapes aslibraries. For highly precise measurement, it is necessary to preparethe libraries having a sufficient amount of data. As a result, theamount of data will become enormous and it will take much time toprepare libraries. Furthermore, in the measurement, the preparedwaveforms must be compared with actual waveforms, and therefore,calculations take much more time than conventional measurementtechniques. The present invention is one of other measurement techniquesthat requires relatively small calculation amount than the technique ofthem.

SUMMARY OF THE INVENTION

In the present invention, a highly precise pattern dimension measurementtechnique which is stable to the changes of the cross-sectional shapesof the pattern as mentioned above is provided, which has been difficultto be realized in the prior technique (technology concerning FIG. 15),and further, the pattern dimension measurement technique in which thecalculation amount can be reduced in comparison to the above-mentionedtechnique (technology concerning FIG. 16) so as to reduce thecalculation time. In addition, it is also possible to evaluate thechanges in the cross-sectional shapes of the pattern in a quantitativemanner. Further, it is also possible to realize a highly precise processcontrol on the basis of these highly precise calculation results.

In a semiconductor pattern measurement method according to the presentinvention, the relationship between the cross-sectional shapes of thepattern and the measurement errors in a specified image processingtechnique are evaluated in advance, and in the actual dimensionmeasurement, dimensions of an evaluation objective pattern are measuredfrom the image signals of a scanning electron microscope, and errors ofthe dimensional measurement of the evaluation objective pattern arerevised on the basis of the relationship between the cross-sectionalshapes of the pattern and the measurement errors evaluated in advance.Further, in a pattern shape evaluation using tilt images, pattern shapesare evaluated in the same manner, and dimension measurement errorsdepending on the shapes are revised, thereby achieving the highlyprecise measurement.

In concrete, the present invention is applied to a semiconductor patternmeasurement method for measuring dimensions of an evaluation objectivepattern by the use of electron beam images of the evaluation objectivepattern that are obtained by a scanning electron microscope. In thismethod, a database is established, in which the relationship between adeviation, i.e., a measurement error between the position of the endportion of a pattern detected by the specified image processingtechnique and the position of the end portion of the actual pattern anda cross-sectional shapes of the pattern is evaluated and recorded inadvance, and in the actual dimension measurement, evaluation of thecross-sectional shapes of the evaluation objective pattern and theposition detection of the end portion of the pattern by the specifiedimage processing technique are carried out, and a measurement error inthe case of measuring a pattern having the cross-sectional shapes isestimated based on the relationship of the cross-sectional shapes of thepattern and the measurement errors recorded in advance in the database,and then, this measurement error is revised.

Further, in the semiconductor pattern measurement method, thecross-sectional shape includes one of a tilt angle of a sidewall, theroundness at a corner of a pattern top portion, and the roundness at acorner of pattern bottom portion, or a combination thereof. Also, thedatabase is established by an electron beam simulation or by the crosssection measurement, the AFM measurement or the measurement byscatterometry. Furthermore, the evaluation of the cross-sectional shapesin the measurement is carried out by the use of the feature quantity ofthe image calculated from SEM images or by the scatterometry.

Furthermore, in the semiconductor pattern measurement method, thecross-sectional structure information of an objective to be measured andthe electron beam images and/or waveforms thereof obtained from an SEMobservation of this cross-sectional structure or a simulation of the SEMobservation are displayed together, and the position of the end portionof the pattern detected by designated image processing conditions isdisplayed on a cross-sectional structure and electron beam images orwaveforms, thereby adjusting the image processing conditions.Furthermore, the dimension measurement image processing algorithms orimage processing parameters whose variations due to the noises or thedevice parameters of SEM are small are employed. Moreover, therelationship between cross-sectional shapes and dimension measurementerrors are recorded in the form of functions into a database.

Further, in the semiconductor pattern measurement method, therelationship between the cross-sectional shapes of the pattern and thesignal waveforms obtained from actual images or an electron beamsimulation are recorded, and after image processing algorithms andparameters of dimension measurement are determined, the relationshipbetween the cross-sectional shapes and dimension measurement errors arecalculated based on a combination of the cross-sectional shapes and thesignal waveforms, and recorded into a database. Furthermore,cross-sectional shapes are evaluated by the use of plural electron beamsignals where angles between an electron beam emitted from a scanningelectron microscope and the surface of a measurement sample aredifferent.

These and other objects, features and advantages of the invention willbe apparent from the following more particular description of preferredembodiments of the invention, as illustrated in the accompanyingdrawings.

BRIEF DESCRIPTIONS OF THE DRAWINGS

FIG. 1A is a flow chart showing dimension measurement procedures whenpreparing measurement recipe, and FIG. 1B is a flow chart showingdimension measurement procedures when measuring an actual pattern;

FIG. 2 is a block diagram showing an SEM to be employed in dimensionmeasurement in a first embodiment of the present invention;

FIG. 3 is an explanatory diagram showing the types of variations in thecross-sectional shape of a pattern in the first embodiment of thepresent invention;

FIG. 4A is an electron beam image showing an area including a linepattern, and the upper stage of FIG. 4B shows a signal waveform of oneline pattern, while the lower stage thereof shows an average signalwaveform obtained by adding N lines;

The upper stage of FIG. 5A shows a cross-sectional shape of the normallytapered pattern, the lower stage thereof shows a signal waveform of anSEM image, while the upper stage of FIG. 5B shows a cross-sectionalshape of the reversely tapered pattern, and the lower stage thereofshows a signal waveform of an SEM image;

FIG. 6 is an explanatory diagram showing details of evaluationprocedures of a pattern shape (flared shape) in the first embodiment ofthe present invention, in which the upper stage shows a cross-sectionalview of a pattern, the middle stage shows a signal waveform of an SEMimage, and the lower stage shows a primary differential waveform of thesignal in the middle stage;

FIG. 7 is an explanatory diagram showing details of evaluationprocedures of a pattern shape (normally tapered shape) in the firstembodiment of the present invention, in which the upper stage shows across-sectional view of a pattern, the middle stage shows a signalwaveform of an SEM image, and the lower stage shows a primarydifferential waveform of the signal in the middle stage;

FIG. 8 is a flow chart showing the procedures of establishing a databasein a second embodiment of the present invention;

FIG. 9A is a flow chart showing procedures of replaying measurementrecipe in a third embodiment of the present invention, and FIG. 9B is aflow chart showing procedures when measuring an actual pattern in thethird embodiment of the present invention;

FIG. 10 is an explanatory diagram showing a display screen for settingimage processing conditions in a fourth embodiment of the presentinvention;

FIG. 11A is an explanatory diagram showing the case where electrondiffusion length in a solid matter is smaller than a pattern top widthin a fifth embodiment of the present invention, in which the upper stageshows a cross-sectional view of a pattern and the lower stage shows asignal waveform of an SEM image, while FIG. 11B is an explanatorydiagram showing the case where electron diffusion length in a solidmatter is larger than a pattern top width in a fifth embodiment of thepresent invention, in which the upper stage shows a cross-sectional viewof a pattern and the lower stage shows a signal waveform of an SEMimage;

FIG. 12 is a block diagram showing an SEM having a tilt imageacquisition function to be used in dimension measurement in a sixthembodiment of the present invention;

FIG. 13A is a diagram showing the relationship between a cross sectionof a pattern and an electron beam incidence direction when acquiringtilt images to be used in the dimension measurement in the sixthembodiment of the present invention, and the upper stage of FIG. 13Bshows an SEM image of a pattern and the lower stage thereof shows asignal waveform of an SEM image;

FIG. 14A is a cross-sectional view of a pattern, and FIG. 14B shows asignal waveform of an SEM image and its maximum inclined point, and FIG.14C shows a signal waveform of an SEM image and its maximum value andminimum value, while FIG. 14D shows a signal waveform of an SEM imageand its slope line;

FIG. 15 is an explanatory diagram showing errors in the case where apattern sidewall is vertical and the case where it is inclined in areferential technology to the present invention;

FIG. 16 is an explanatory diagram showing the relationship between thetilt angles of the pattern sidewall and errors in the pattern dimensionmeasurement in a referential technology to the present invention; and

FIG. 17 is a block diagram showing a manufacturing line system connectedto the network in a seventh embodiment of the present invention.

DESCRIPTIONS OF THE PREFERRED EMBODIMENTS

Hereinafter, embodiments of the present invention will be described indetail with reference to the accompanying drawings. Note that componentshaving the same function are denoted by the same reference symbolsthroughout the drawings for describing the embodiment, and therepetitive description thereof will be omitted.

(First Embodiment: Basic Structure)

(Entire Process Flow)

FIG. 1 is a schematic diagram of measurement procedures using a patternmeasurement system established on a length measuring SEM 200 (whoseschematic structure is shown in FIG. 2) in a first embodiment of thepresent invention. In an example of this embodiment, two steps arerequired, that is, the measurement recipe preparing step for preparing ameasurement recipe in which measurement conditions and procedures forthe automatic measurement are recorded (FIG. 1A), and the step ofactually measuring a measurement objective pattern (FIG. 1B).

First, the procedures in the step of preparing measurement recipe (FIG.1A) will be described below. When preparing the measurement recipe, adatabase 401 in which the relationship between the cross-sectionalshapes of a measurement objective pattern and dimension measurementerrors is recorded is prepared. In the example of FIG. 1, a method usingan electron beam simulation will be described.

First, cross-sectional shape data of the measurement objective isprepared (step 1001). Here, changes in cross-sectional shapes of ameasurement objective pattern in an actual semiconductor manufactureprocess are estimated and the models of representative shapes areprepared. In FIG. 1, for simplicity, as cross-sectional shape data, acase where only a tilt angle θ of a sidewall changes is considered. Inconsideration of change range of sidewall tilt angle of an actualpattern, for example, if the sidewall tilt angle θ changes in the rangefrom 80 degrees to 95 degrees, several through ten and several pieces ofcross-sectional shape data of the sidewall tilt angle are preparedwithin the range. Note that, in FIG. 1, only the data for one sidewallof a wire pattern is shown. When pattern dimension is small, it isnecessary to consider adjacent sidewall portion, and details of theestablishment of this database 401 will be described in the fifthembodiment.

Further, for the cross-sectional shape data, an SEM image signalobtained by the use of an electron beam simulator is generated (step1002). In an electron beam simulation, secondary electron signalsrelative to electron beam radiation conditions (acceleration voltage andthe likes) in the actual measurement may be calculated by means of MonteCarlo method and so forth. Next, an image process employed in the actualmeasurement is conducted to the simulation results of SEM image signalsobtained as described above, and the positions of pattern edge aredetected (step 1003). With regard to the method of setting algorithms,details thereof will be described separately in the fourth embodimentlater.

Then, in the simulation, the positional difference (error: ΔW (θ))between a detected edge and an actual edge (for example, the position ofa pattern bottom edge) can be easily calculated (step 1004). In thismanner, it is possible to know what errors the used dimensionmeasurement algorithm has relative to the variations of cross-sectionalshapes. In the recipe preparation, the relationship between thecross-sectional shape of the pattern and the edge position detectionerrors obtained in this manner are registered into the database (step1005).

In FIG. 1, the relationship between the sidewall tilt angles θ andmeasurement errors are displayed schematically in a graph. However, inan actual database, sets of prepared data are recorded as they are, andthe sidewall tilt angles θ other than the prepared data are used byinterpolating data, alternatively, a relation expression may becalculated out in the form of a function from these data sets, and theresulting function is recorded and used.

Subsequently, procedures to carry out actual measurement by the use ofthis database (FIG. 1B) will be described. The measurement is conductedby acquiring an SEM image at a desired measurement position in the samemanner as conventional dimension measurement by an SEM (step 1006).Next, from the obtained SEM image, dimension (line width W′) of anobjective pattern and estimated values of sidewall tilt angles (leftedge θl, right edge θr) are obtained (step 1007). This estimation ofthese sidewall tilt angles is to be described separately later. Next,the error of this line width W′ is estimated by the use of estimatedsidewall tilt angle and the database prepared when preparing the recipe,and revised by W=W′−ΔW(θl)−ΔW(θr) (step 1008). By outputting thedimension measurement result revised in this manner, it becomes possibleto obtain a dimension with small error due to the variation of thecross-sectional shape of the pattern (step 1009).

(System Block Diagram of an SEM Main Body)

FIG. 2 is a block diagram of a length measuring SEM 200 to be employedin this pattern shape evaluation system. This length measuring SEM 200comprises an electron gun 201, a condenser lens 203, a deflector 204, anExB deflector 205, an objective lens 206, a secondary electron detector207 and so forth, and is connected via an A/D converter 208 to an imageprocessing unit 300.

In FIG. 2, a primary electron beam 202 that is emitted from the electrongun 201 is converged by the condenser lens 203 and is radiated via thebeam deflector 204, the ExB deflector 205, the objective lens 206, tofocus on a wafer 100 put on a stage 101. When the electron beam isradiated, secondary electrons are generated from the wafer 100. Thesecondary electrons generated from the sample wafer 100 are deflected bythe ExB deflector 205 and are detected by a secondary electron detector207. By detecting electrons generated from the wafer in synchronizationwith two-dimensional scanning of electron beam by the deflector 204 orrepeated scanning of electron beam in an X direction by the deflector204 and continuous movement of the wafer 100 in a Y direction by thestage 101, a two-dimensional electron beam image is obtained. Signalsdetected by the secondary electron detector 207 are converted intodigital signals by the A/D converter 208 and transferred to the imageprocessing unit 300.

This image processing unit 300 includes an image memory that temporarilystores digital images and a CPU that calculates line profiles andfeature quantity from images on the image memory. Further, it has amemory medium 301 that stores detected images or line profiles orcalculated pattern shape information and so forth. A display device 302is connected to the image processing unit so as to operate the necessarydevices and confirm the detection results by the graphical userinterface (hereinafter referred to as GUI).

(Acquisition of Solid Shape Information)

Next, calculation procedures of solid shape information to be conductedby the image processing unit 300 will be described with reference toFIGS. 3 to 7. As shown in FIG. 3 a, major kinds of the variations incross-sectional shape of a pattern include a pattern width (top, bottom,or arbitrary height and the likes), height, sidewall tilt angle, cornerroundness (top, bottom) and so forth. The dimension to be measured hereis width, and unless otherwise specified, bottom width W is measured inthe descriptions of the present invention. In the case of measuring thispattern width W by the use of SEM image, it is necessary to revise themeasurement errors shown in FIG. 1 in consideration of the influentialshape variations.

In the case where a pattern is formed with using a film made ofdifferent material as a stopper like the etching of a gate wireperformed with using a gate insulator as a stopper, since the thicknessof pattern height is controlled in a film forming process before thepattern forming process by exposure/etching, its variation issufficiently small and its influence upon dimension measurement issmall. Accordingly, in the case of such a pattern, variations ofremaining sidewall tilt angles and corner roundness will give relativelylarge influence to dimension measurement using the SEM image. However,the magnitude of influences that these variations give to themeasurement results varies depending on the image processing algorithmsto be used in the measurement. In this case, the image processingalgorithms to be used in the measurement are image processing techniquesas shown in FIG. 14, and it is necessary to select appropriate onesaccording to the objective patterns.

A method to evaluate this sidewall tilt angle and corner roundness in aquantitative manner will be described. First, as shown in FIGS. 4A and4B, in order to improve S/N, with regard to acquired electron beam image001, N lines of one line waveform 002 of each line are averaged tocreate a smooth line profile 003. This line profile 003 shows signalamount according to sidewall shape of the patter.

Details of the relationship between this signal amount and thecross-sectional shapes of the pattern will be described with referenceto FIG. 5. It is known that the secondary electron signal amount of SEMchanges in accordance with sidewall tilt angles, and the larger the tiltangle is, the larger the secondary electron signal amount becomes.Therefore, as shown in FIG. 5A, when there is no flared portion in across-sectional shape 010 and the entire sidewall keeps a relativelyhigh tilt angle, the line profile 011 increases rapidly from the bottomedge, while as shown in FIG. 5B, when there is a flared portion in across-sectional shape 020, the secondary electron signal amount of aflared portion 023 becomes smaller than that of high tilt angle portion(upper portion) 022 having a relatively high tilt angle. Further, alsowhen there is roundness in top corner, the signal amount change of theportion corresponding to a corner becomes smoother than that in the casewithout roundness ((b) is smoother than (a)). This is because the changeof surface tilt angle is moderate, and rapid signal amount increase isrestricted by the edge effect because the corner is rounded.

By using these relationships, cross-sectional shape information isacquired in the following procedures. First, by separating the portionwith relatively small signal amount and the portion with relativelylarge signal amount, a cross-sectional shape is divided into a highinclined portion 022 and a flared portion 023, and a top rounding(roundness) portion 024 only by the SEM image observed from the above ofa sample (wafer) (see FIG. 5B.). FIG. 6 is detailed explanations of FIG.5B and FIG. 7 is detailed explanations of FIG. 5A. As shown in FIG. 6,when a primary differential waveform 025 of the obtained line profile021 is prepared, the waveform having extreme values (DLP1, DLP2) at thepositions where lightness changes rapidly in the original line profile021 is formed. Therefore, the portion between these extreme values DLP1and DLP2 corresponds to a high tilt angle portion 022 whose tilt angleis relatively high in the sidewall.

Then, the distance between these extreme values is set as a tilt angleindex value T. Meanwhile, the distance from the outer extreme value(DLP1) of the differential waveform of an edge portion to the pointwhere the differential waveform becomes zero (DZ1), i.e., the pointwhere it becomes the same lightness as the substrate shows a flaredportion where the tilt angle is relatively low. Therefore, the distancebetween them is set as a flare index value F. Further, the distance fromthe inner extreme value (DLP2) of differential waveform of an edgeportion to the point where the differential waveform becomes zero (DZ2),i.e., to the point where it becomes the same lightness as the topportion represents a top round portion where the tilt angle isrelatively low. Therefore, the distance between them is set as arounding index value R.

Similarly, the result of acquisition of index values of the shape inFIG. 5A is shown in FIG. 7 (primary differential waveform 015) (indexvalues of flared portion and rounding are not illustrated therein). Asis seen by the comparison of FIG. 6 and FIG. 7, when the pattern heightH is constant, the tilt angle index value T is proportional to tan (π−θ)and becomes smaller as θ becomes closer to vertical. In the case of areverse taper, the information of sidewall portion vanishes, and onlythe portion by the edge effect is detected. Accordingly, the tilt angleindex value T remains to be a constant value. On the contrary, the flareindex value F becomes larger as the flared portion becomes larger, whilethe rounding index value R becomes larger as roundness becomes larger.In this manner, it is possible to obtain the solid shape information ofthe pattern by these index values.

Note that the sidewall tilt angle θ can be obtained the following(Equation 1).θ=π/2−a tan((T-T0)/H   (Equation 1)Herein, T0 is a tilt angle index value that is observed when thesidewall tilt angle is 90 degrees (vertical).

In the embodiment described above, the signal waveform is divided into ahigh tilt angle portion and a low tilt angle portion by the use ofprimary differential value. However, it is possible to obtain similareffect by dividing the profile waveform area by the value of signalamount itself by using an appropriate threshold value.

As described above, solid shape information of the cross section of theobtained pattern is acquired and is combined with the previouslyprepared information in a database showing the relationship between thesolid shape variations and dimension measurement errors caused by thevariations. In this manner, the dimension measurement errors arereduced. In the example of FIG. 1 mentioned above, from SEM image of themeasurement objective pattern obtained in the dimension measurement, thesidewall tilt angle θ is estimated by (Equation 1), and the dimensionmeasurement errors may be revised with reference to the database.

Further, instead of using the tilt angle θ itself, feature quantity ofan image showing a tilt angle (tilt angle index value T) can be used. Inthis case, in the same manner as shown in FIGS. 5 to 7, the featurequantity of an image showing tilt angle is calculated also for theelectron beam simulation result in the recipe preparation, and therelationship between the feature amount of the image showing this tiltangle and the measurement errors may be recorded into the databaseinstead of the actual θ. As described above, when a database isestablished by the use of not the actual shape but the same featurequantity of an image as that used in the dimension measurement, a morestable result can be obtained.

By the way, in the first embodiment shown in FIG. 1, only the sidewalltilt angle θ is used as the cross-sectional shape information of apattern. However, if the variations of roundness of pattern cornerportion mentioned above is large or the dimension measurement imageprocessing algorithms to be used tend to be influenced by the variationsof the corner roundness, it is necessary to combine the evaluations bythe index values thereof. In this case, the relationship between theshape parameters and measurement errors is not two-dimensional like thatin FIG. 1, but may be multi-dimensional. At this moment, roundness ofcorner to be input to the electron beam simulation may be given by, forexample, the curvature radius of corner portion. As the shape parameter,the curvature radius input to the simulation may be used as it is, orthe feature quantity of an image (flare and rounding index values F andR) calculated from the SEM image signal waveform obtained from theelectron beam simulation may be employed.

Further, in the embodiment described above, the relationship between thecross-sectional shape information (for example, sidewall tilt angle) andthe measurement errors is recorded into a database. However, in the casewhere there is the possibility that the image processing conditions maybe changed later, a combination of the cross-sectional shapes and theSEM signal waveforms obtained by an electron beam simulation may berecorded, and when determining the image processing conditions, therelationship between the cross-sectional shapes and the dimensionmeasurement errors may be calculated and added also to the database.

In the foregoing, the first embodiment has been described in detailswith reference to FIGS. 1 to 7, and according to this embodiment, it ispossible to revise the errors in the pattern dimension measurement evenfor the variations of cross-sectional shapes such as pattern sidewalltilt angle. Consequently, highly precise dimension measurement can berealized, and also, the highly precise measurement and highly preciseprocess control using the results thereof can be achieved for thesemiconductor patterns which have been scaled down rapidly. In the firstembodiment, it is not necessary to minutely carry out a simulation forthe establishment of the database to all the possible cross-sectionalshape variations, and the number of calculations in the simulation canbe smaller in comparison to that in the method in which the librariesare prepared from the simulation result. Furthermore, the database isonly referenced to based on the solid shape information estimated fromthe SEM image, it is advantageous that the number of calculations can bereduced in the measurement.

(Second Embodiment: Database Establishment by Actual Sample or AFM)

Next, a second embodiment will be described with reference to FIG. 8. Inthe first embodiment, a database of the relationship between thecross-sectional shapes of the pattern and the dimension measurementerrors is established by the use of an electron beam simulation.However, in the second embodiment, an example is shown, in whichpatterns of various shapes are prepared actually and a database isestablished by the use of images of these patterns observed actually bySEM.

FIG. 8 shows the procedures of establishing the database. First,patterns for simulating the actually possible process variationsaccording to plural process conditions (exposure condition for a resistpattern, etching condition for an etching pattern, and so forth) areformed (step 1020). Further, SEM images of these patterns are acquiredunder the same conditions as those at the actual measurement (step1021), and position detection (dimension measurement) of each edge iscarried out under specific image processing conditions (step 1022).Next, in order to evaluate these edge position detection errors, actualcross-sectional shapes are measured by the use of a cross section SEMand an FIB, a TEM and the likes (step 1023). By the use of thecross-sectional shapes obtained in the above-described manner and (true)pattern dimensions, the errors in the line width measurement arecalculated (step 1024), and the relationship between these errors andthe cross-sectional shapes (for example, sidewall tilt angle) arerecorded into a database (step 1025). By doing so, it is possible toestablish a database for realizing highly precise dimension measurementwithout errors due to the variations n the cross-sectional shape in thesame manner as in the first embodiment. Note that, the evaluation of theactual cross-sectional shapes may be of course performed by the use ofAFM and the like other than the cross section observation.

In the second embodiment, the cost is increased due to the samplepreparation and evaluation in comparison to that in the firstembodiment. However, it has an advantage that more highly reliableresults can be obtained without depending on the performance of anelectron beam simulation.

(Third Embodiment: Use of Scatterometry)

Next, a third embodiment will be described with reference to FIG. 9. Inthe first embodiment, a database of the relationship between thecross-sectional shapes of the pattern and the dimension measurementerrors is established by the use of an electron beam simulation, and theevaluation of the solid shapes of objective patterns is carried out bythe use of the feature quantity of the image obtained from SEM images.However, in the third embodiment, an example is shown, in which theestablishment of a database and the evaluation of the cross-sectionalshape of the pattern in the measurement are carried out by the use ofscatterometry (shape evaluation technique using scattered light).

The establishment of a database is carried out in the same manner as inthe second embodiment, while the evaluation of cross-sectional shape(step 1030) is carried out not by the cross section observation or AFMbut by the scatterometry. In the measurement, first, the evaluation ofcross-sectional shape is carried out by scatterometry (step 1031),thereafter, the dimension measurement by an SEM and the error revisionare conducted in the same manner as in the first embodiment. Note thatthe establishment of the database can be carried out by an electron beamsimulation in the same manner as in the first embodiment.

By the scatterometry, more detailed cross-sectional shape informationcan be obtained than by an SEM, but only average information of patternsin a relatively wide area can be obtained. However, in the case wheremany transistors are arrayed, it is necessary to evaluate thesedimensional variations, and the average dimension evaluation is notsufficient. Therefore, by using them in combination as in the thirdembodiment, in addition to the advantages in the first embodiment,another advantage can be obtained that it is possible to highly andprecisely measure the local pattern dimensions at a desired position bythe use of more highly reliable cross-sectional shape information.

(Fourth Embodiment: Setting of Image Processing Condition+GUI,Measurement under Stable Conditions on Premise of Revision)

Next, a fourth embodiment will be described with reference to FIG. 10.In the first embodiment, details about image processing algorithms forthe dimension measurement are not specifically described. However, ifthe selection of image processing algorithm and designation ofmeasurement position are performed in accordance with a technique shownin the fourth embodiment, it is possible to carry out further highlyreliable measurement.

First, screen display for setting the measurement position will bedescribed with reference to FIG. 10. As shown in FIG. 10, across-sectional structure 402 of a measurement objective pattern and itsSEM image 403 or its signal waveform 404 are displayed side by side on ascreen, and a measurement result 405 under designated image processingconditions is displayed on both the images. In this case, the SEM image403 may be either an actual image or an electron beam simulation imageused in the first embodiment. By displaying the images side by side asshown in FIG. 10, it is possible to easily confirm which portion ismeasured on the signal waveform 404 and the actual cross-sectionalstructure 402 under the current image processing conditions. Here, thesetting of a position on the cross-sectional structure to be actuallymeasured can be easily made by a mouse pointer 406. This measurementposition and an actually measured position and the differencetherebetween are displayed as the measurement result 405 on the screen.Further, an image processing condition 407 at this moment is alsodisplayed.

Next, a method of setting the image processing conditions by the use ofthis screen will be described. In the present invention, thecross-sectional shape dependency of the dimension measurement results isrevised. Therefore, even if a dimension measurement error is large,revision can be easily made as long as the relationship with thecross-sectional shape is stable. Therefore, besides the prior techniquesshown in FIG. 14 such as the threshold method and the likes, the patternedge detection where the maximum value of an image signal, the minimumvalue thereof, the maximum value of a differential signal, the minimumvalue thereof, intersection with zero and so forth are used as thefeature points in an image signal is carried out, and the conditionswhere dimension measurement results are stable are adopted from amongthese. The determination of algorithms can be achieved by performing anelectron beam simulation in consideration of variations of electron beamdiameter to be radiated and random noise that occur in actual electronbeam radiation and by selecting the dimension measurement algorithmsthat are stable to these variations.

Consequently, by setting the image processing algorithm and theconditions according to this embodiment, besides the advantagesdescribed in the first embodiment, it is possible to realize furtherstable error revision and highly precise measurement.

(Fifth Embodiment: DB Preparation Method, Waveform DB+Condition Change)

Next, a fifth embodiment will be described with reference to FIG. 11. Inthe present invention, the measurement using electron beam imagesobtained by an SEM is carried out. When acquiring the electron beamimage, the radiated primary beam diffuses in a solid matter as ameasurement objective, and secondary electrons are generated. As shownin FIGS. 11A and 11B, if the wire width is small in comparison to adiffusion length 408 of the radiated electrons in the solid matter,since the information of the signal waveform at the pattern end isinfluenced by the edges at the opposite side, it is required to carryout an electron beam simulation in consideration of the dimensions. Forexample, in the case of the acceleration voltage about 1 keV, since thediffusion length is several tens nm, attention must be paid when thepattern dimension is less than 100 nm (FIG. 11B). On the other hand,when the pattern dimension is sufficiently large in comparison to thediffusion length 408, there is no need to consider dimensions (FIG.11A).

Accordingly, in the fifth embodiment, diffusion length 408 of electronsin a measurement objective pattern under the conditions of imageacquisition is searched in advance by a simulation or an experiment, andin the case of a pattern smaller than that, a database using asimulation in consideration of the dimensions or the result of actualsample evaluation is established, and in the case of a pattern largerthan that, a database using a simulation in consideration of only oneside edge or the result of actual sample evaluation is establishedregardless of the dimensions. At this time, a database is prepared foreither left edge or right edge, and data may be reversed for the edge atthe opposite side. Even in the case of a small dimension, the influencefrom the edge shape at the opposite side is not so large, though it isnecessary to consider the pattern dimension, electron beam simulationwaveform may be prepared for only one side edge on assumption that ithas a symmetrical shape. Namely, as the input data of the simulation, apattern having representative dimensions (for example, designdimensions) is formed, while the simulation may be carried out for onlyone side edge.

As mentioned above, according to the fifth embodiment, since a databaseis appropriately established in consideration of the electron beamradiation conditions and the dimensions of evaluation objective, itbecome possible to attain a further highly precise and reliabledimension measurement. Especially, if the patterns are relatively large,it is possible to revise the errors and measure the dimensions of thepatterns by the use of the same database even when the patterns havedifferent dimensions.

(Sixth Embodiment: Use of Tilt Image, Adapted for Reverse Taper)

Next, a sixth embodiment will be described with reference to FIG. 12 andFIG. 13. Since where normal SEM images are employed in the firstembodiment, the measurement cannot be made for the sidewall tilt angleexceeding 90 degrees, i.e., for the reverse taper. Therefore, as shownin FIG. 12, the length measuring SEM employed in this embodiment canmove on an XY plane and further has a tilt stage 102 provided with atilt function, and therefore, it can obtain the tilt images, besides thenormal top-down view images. Other structures are identical to those inthe first embodiment.

In a tilt image, the number of pixels increase at the portioncorresponding to the left resist sidewall, while it decreases at theportion corresponding to the right sidewall (in the case whereinclination of the tilt stage rises to the right to a sample).Accordingly, this embodiment focuses on a line profile at the portioncorresponding to the sidewall on the side where the number of pixelsincreases. As shown in FIGS. 13A and 13B, by tilting a measurementobjective to a beam at a specific angle, an offset according to the tiltangle is made in the feature quantity of an image (tilt angle indexvalue T) showing the tilt angle described in the first embodiment. As aresult, it becomes possible to estimate a tilt angle even in the reversetaper. Further, in this embodiment, by acquiring plural images havingdifferent tilt angles, the cross-sectional shape of a pattern can beestimated by the principle of stereo vision. As described above, whenplural images having different tilt angles are used, it becomes possibleto measure the pattern height (or depth). Therefore, for a patternwithout etch stopper, it becomes possible to consider the errors in thedimension measurement due to the variations in the pattern height. Notethat, instead of tilting a stage, also by tilting a column of anelectron optical system or by changing the deflection angle of radiatedelectron beam, incidence angle to a sample (wafer) can be of coursechanged.

By the combination of the solid shape evaluation according to thisembodiment with the first and second embodiments, in addition to theeffects described in the above-described embodiments, the measurement ofthe patterns having reversely tapered sidewalls and the measurement ofthe patterns having height variations that cannot be made by thetop-down view can be achieved by using the tilt images.

Note that, in the present invention, patterns of wires have been madeobjectives to be measured in the embodiments mentioned above. However,it is needless to say that it is possible to revise the dimensionmeasurement errors and carry out the highly precise measurement also forother pattern shapes such as holes, trenches, and the likes in the samemanner.

(Seventh Embodiment: Process Monitor/Control)

Next, the process control using the dimension measurement andcross-sectional shape evaluation method according to the presentinvention will be described in this embodiment. In the embodimentsmentioned above, the pattern shape evaluation function and the dimensionmeasurement function are loaded on the SEM 200. However, it issufficient that the SEM 200 can acquire images, and these functions maybe loaded on a system connected via a network. FIG. 17 shows an exampleof a system connected via a network. The SEM to realize the presentinvention is connected to various manufacturing apparatus and devicecharacteristic evaluation apparatus all via network and these areconnected to an apparatus control system 500 and a QC data collectionand analysis system 501. The manufacture history is controlled by amanufacture history control system 504.

A defect monitoring system 502 monitors the defects in them, and amanufacturing line controller 505 can easily confirm the condition ofthe manufacturing line via a display and communication unit 503. Thedimension measurement of the present invention can be executed by theuse of an existing SEM in a manufacturing line. Since the system isconnected to the manufacturing apparatus via a network, by setting thenormal dimensions and shape variation range in advance, a warning can besent to the line controller immediately when a defect exceeding themoccurs. Further, by registering the related manufacturing apparatus inadvance, the manufacture can be stopped automatically to prevent thedefective products from being manufactured.

Furthermore, by registering the target values of the dimensionmeasurement and the solid shape evaluation result according to thepresent invention and the process conditions to revise the deviatedamounts from the target values (etching conditions and exposureconditions) in advance into the QC data collection and analysis system501 or the apparatus control system 500, the processes can be controlledby the apparatus control system 500 so as to revise the variations inthe pattern dimensions and pattern shape. In this manner, by controllingeach apparatus so as to revise process variations, it becomes possibleto always process the patterns highly precisely and to enhance the yieldof a semiconductor manufacturing line.

According to the present invention, it is possible to realize highlyprecise measurement in which errors in dimensions depending on thepattern solid shapes are removed. As a result, it is possible to carryout a highly precise process control.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentembodiment is therefore to be considered in all respects as illustrativeand not restrictive, the scope of the invention being indicated by theappended claims rather than by the foregoing description and all changeswhich come within the meaning and range of equivalency of the claims aretherefore intended to be embraced therein.

1. A method of measuring pattern dimensions, comprising: a first step inwhich a relationship between cross-sectional shapes of a pattern andmeasurement errors of a pattern is evaluated in a specified imageprocessing technique; and a second step in an actual measurement inwhich dimension measurement of an evaluation objective pattern fromimage signals of a scanning electron microscope is carried out, anderrors of the dimension measurement of said evaluation objective patternis revised based on said relationship between the cross-sectional shapesof a pattern and the measurement errors of a pattern evaluated inadvance.
 2. The method of measuring pattern dimensions according toclaim 1, wherein a database in which the relationship betweenmeasurement errors between a pattern position detected by the specificimage processing technique and actual position of the pattern and thecross-sectional shapes of said pattern is evaluated and recorded inadvance is established in said first step, and cross-sectional shapes ofsaid evaluation objective pattern are evaluated and the position of saidevaluation objective pattern is detected by said specified imageprocessing technique, and measurement errors in the case of measuringthe evaluation objective patterns having said cross-sectional shapes areestimated and said measurement errors are revised based on therelationship between said pattern cross-sectional shapes and saidmeasurement errors recorded in advance into said database in said secondstep.
 3. The method of measuring pattern dimensions according to claim2, wherein said cross-sectional shape includes one of a tilt angle of asidewall, roundness of a corner of pattern top portion, and roundness ofa corner of pattern bottom portion, or a combination thereof.
 4. Themethod of measuring pattern dimensions according to claim 2, whereinsaid database is established by an electron beam simulation.
 5. Themethod of measuring pattern dimensions according to claim 2, whereinsaid database is established by a cross section measurement, an AFMmeasurement, or a measurement by scatterometry.
 6. The method ofmeasuring pattern dimensions according to claim 2, wherein saidevaluation of cross-sectional shapes in said actual dimensionmeasurement is carried out by the use of feature quantity of an imagecalculated from SEM images.
 7. The method of measuring patterndimensions according to claim 2, wherein said evaluation ofcross-sectional shapes in said actual dimension measurement is carriedout by scatterometry.
 8. The method of measuring pattern dimensionsaccording to claim 2, wherein cross-sectional structure information ofan objective to be measured and electron beam images and/or waveformsthereof obtained from an SEM observation of said cross-sectionalstructure or a simulation that simulates the SEM observation aredisplayed side by side, and a position of the pattern detected bydesignated image processing conditions is displayed on saidcross-sectional structure and said electron beam images or saidwaveforms, thereby adjusting image processing conditions.
 9. The methodof measuring pattern dimensions according to claim 2, wherein saidspecified image processing technique uses dimension measurement imageprocessing algorithms or image processing parameters in which variationsdue to noises or SEM device parameters are small.
 10. The method ofmeasuring pattern dimensions according to claim 2, wherein therelationship between said pattern cross-sectional shapes and saiddimension measurement errors is recorded in the form of functions intothe database.
 11. The method of measuring pattern dimensions accordingto claim 2, wherein, with regard to the establishment of said database,the relationship between said pattern cross-sectional shapes and signalwaveforms obtained from actual images or an electron beam simulation isrecorded, and after determining image processing algorithms andparameters of dimension measurement, the relationship between saidcross-sectional shapes and said dimension measurement errors arecalculated based on a combination of said cross-sectional shapes andsaid signal waveforms, and recorded into said database.
 12. The methodof measuring pattern dimensions according to claim 2, whereincross-sectional shapes of said evaluation objective pattern areevaluated by the use of plural electron beam signals in which anglesbetween an electron beam emitted from said scanning electron microscopeand a surface of the measurement sample are different.
 13. A method ofcontrolling semiconductor device manufacturing process, wherein defectsin process are detected and the process is controlled based on patternmeasurement results obtained by using said measuring method of patterndimensions described in claim 1.